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课程大纲
Foundations of Sovereign AI
- What sovereign AI means in regulated organizations
- Business, legal, and operational drivers
- Core control areas: data, models, infrastructure, and operations
Regulatory Requirements and Risk Mapping
- Data residency, privacy, and sector-specific obligations
- Mapping sensitive data to AI use cases
- Identifying cross-border, logging, and third-party exposure risks
Governing Data, Prompts, and Logs
- Prompt governance and acceptable use boundaries
- Logging policies for prompts, responses, and metadata
- Retention, redaction, masking, and access control practices
- Exercise: reviewing an AI data flow for governance gaps
Model Hosting and Inference Environment Options
- Public API, private cloud, on-premise, and hybrid deployment choices
- Factors for deciding where models should run
- Trade-offs among control, security, cost, and operational ownership
Vendor Dependence and Portability
- Common lock-in patterns in models, tools, and platforms
- Portability through modular architecture, open interfaces, and clear contracts
- Exercise: evaluating a vendor against sovereignty criteria
Governance Model and Action Planning
- Roles and responsibilities across IT, security, legal, and compliance
- Approval workflows for use cases, models, and operational changes
- Auditability, monitoring, and incident response expectations
- Building a practical sovereign AI roadmap and next steps
要求
- A basic understanding of AI concepts, data governance, and compliance requirements
- Familiarity with enterprise technology, cloud, security, or risk decision-making
- No programming experience is required
Audience
- IT leaders, enterprise architects, and platform managers
- Risk, compliance, legal, and data governance professionals
- Security teams and business leaders responsible for AI adoption in regulated environments
7 小时